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Using a smartphone app, the investigators recruited 419,297 participants to be monitored for irregular pulses. Patterns suggesting atrial fibrillation were detected in 2161 participants who then received ECG monitoring devices to be worn for 7 days to confirm the presence or absence of atrial fibrillation.

Smartwatch and fitness band wearable consumer electronics can passively measure pulse rate from the wrist using photoplethysmography (PPG). Identification of pulse irregularity or variability from these data has the potential to identify atrial fibrillation or atrial flutter (AF, collectively). The rapidly expanding consumer base of these devices allows for detection of undiagnosed AF at scale. The Apple Heart Study is a prospective, single arm pragmatic study that has enrolled 419,093 participants (NCT03335800). The primary objective is to measure the proportion of participants with an irregular pulse detected by the Apple Watch (Apple Inc, Cupertino, CA) with AF on subsequent ambulatory ECG patch monitoring. The secondary objectives are to: 1) characterize the concordance of pulse irregularity notification episodes from the Apple Watch with simultaneously recorded ambulatory ECGs; 2) estimate the rate of initial contact with a health care provider within 3 months after notification of pulse irregularity. The study is conducted virtually, with screening, consent and data collection performed electronically from within an accompanying smartphone app. Study visits are performed by telehealth study physicians via video chat through the app, and ambulatory ECG patches are mailed to the participants. The results of this trial will provide initial evidence for the ability of a smartwatch algorithm to identify pulse irregularity and variability which may reflect previously unknown AF. The Apple Heart Study will help provide a foundation for how wearable technology can inform the clinical approach to AF identification and screening.

COVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensity the thrombotic burden in patients with COVID-19. However, venous thromboembolism, arterial thrombosis, and other vascular complications may go unnoticed in critical care settings. Early risk stratification is paramount in the COVID-19 patient population for proactive monitoring of thrombotic complications. The aim of this exploratory research was to characterize thrombotic complication risk factors associated with COVID-19 using information from electronic health record (EHR) and insurance claims databases. The goal is to develop an approach for analysis using real-world data evidence that can be generalized to characterize thrombotic complications and additional conditions in other clinical settings as well, such as pneumonia or acute respiratory distress syndrome in COVID-19 patients or in the intensive care unit. We extracted deidentified patient data from the insurance claims database IBM MarketScan, and formulated hypotheses on thrombotic complications in patients with COVID-19 with respect to patient demographic and clinical factors using logistic regression. The hypotheses were then verified with analysis of deidentified patient data from the Research Patient Data Registry (RPDR) Mass General Brigham (MGB) patient EHR database. Data were analyzed according to odds ratios, 95% CIs, and P values. The analysis identified significant predictors (P<.001) for thrombotic complications in 184,831 COVID-19 patients out of the millions of records from IBM MarketScan and the MGB RPDR. With respect to age groups, patients 60 years and older had higher odds (4.866 in MarketScan and 6.357 in RPDR) to have thrombotic complications than those under 60 years old. In terms of gender, men were more likely (odds ratio of 1.245 in MarketScan and 1.693 in RPDR) to have thrombotic complications than women. Among the preexisting comorbidities, patients with heart disease, cerebrovascular diseases, hypertension, and personal history of thrombosis all had significantly higher odds of developing a thrombotic complication. Cancer and obesity were also associated with odds>1. The results from RPDR validated the IBM MarketScan findings, as they were largely consistent and afford mutual enrichment. The analysis approach adopted in this study can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped to identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients represents only a case study; however, the same design can be used across other disease areas by extracting corresponding disease-specific patient data from available databases.

Improving adherence to direct oral anticoagulants (DOAC) is challenging, and simple text messaging reminders have not been effective. SmartADHERE was a randomized trial that tested a personalized digital and human direct oral anticoagulant adherence intervention compared to usual care. Eligibility required age ≥ 18, newly-prescribed (≤90 days) rivaroxaban for atrial fibrillation (AF), 1 of 4 at-risk criteria for nonadherence, and a smartphone. The intervention consisted of combination of a medication management smartphone app, daily app-based reminders, adaptive text messaging, and phone-based counseling for severe nonadherence. The primary outcome was the proportion of days covered by rivaroxaban (PDC) at 6 months. There were 25 U.S. sites, all cardiology and electrophysiology outpatient practices, activated for a target sample size of 378, but the study was terminated by the sponsor prior to reaching target enrollment. There were 139 participants (age 65±9.6 years, 30% female, median CHA2DS2-VASc score 3 with IQR 2 to 4, mean total medication burden 7.7±4.4). DOAC adherence was high in both arms with no difference in the primary outcome (PDC 0.86±0.25 intervention vs 0.88±0.25 control, p=0.62) or in secondary outcomes including PDC ≥ 0.80 and medication persistence. Per protocol analyses had similar results. Because of the high overall PDC, the likelihood to answer the primary hypothesis was only 51% even if target enrollment were achieved. There were no study-related adverse events. The use of a centralized digital and human adherence intervention was feasible across multiple sites. Overall adherence was much higher than expected despite prescreening for at-risk individuals. SmartADHERE illustrates the challenges of trials of behavioral and technology interventions, where enrollment itself may lead to selection bias or treatment effects. Pragmatic study designs, such as cluster randomization or stepped-wedge implementation, should be considered to improve enrollment and generalizability.

BackgroundThe first surge of the COVID-19 pandemic entirely altered healthcare delivery. Whether this also altered the receipt of high- and low-value care is unknown.ObjectiveTo test the association between the April through June 2020 surge of COVID-19 and various high- and low-value care measures to determine how the delivery of care changed.DesignDifference in differences analysis, examining the difference in quality measures between the April through June 2020 surge quarter and the January through March 2020 quarter with the same 2 quarters’ difference the year prior.ParticipantsAdults in the MarketScan® Commercial Database and Medicare Supplemental Database.Main MeasuresFifteen low-value and 16 high-value quality measures aggregated into 8 clinical quality composites (4 of these low-value).Key ResultsWe analyzed 9,352,569 adults. Mean age was 44 years (SD, 15.03), 52% were female, and 75% were employed. Receipt of nearly every type of low-value care decreased during the surge. For example, low-value cancer screening decreased 0.86% (95% CI, −1.03 to −0.69). Use of opioid medications for back and neck pain (DiD +0.94 [95% CI, +0.82 to +1.07]) and use of opioid medications for headache (DiD +0.38 [95% CI, 0.07 to 0.69]) were the only two measures to increase. Nearly all high-value care measures also decreased. For example, high-value diabetes care decreased 9.75% (95% CI, −10.79 to −8.71).ConclusionsThe first COVID-19 surge was associated with receipt of less low-value care and substantially less high-value care for most measures, with the notable exception of increases in low-value opioid use.

Web logs record the users search queries and related actions in search engines. It is possible to understand user search behaviors by mining these information. A task can be defined as atomic user information need, whereas a task track represents activities of all user within that particular task, such as query reformulations, URL clicks. In the previous works, web logs have been studied at session, query or task level where users have to submit several queries within one task and handle several tasks within a session. Although previous studies have addressed the problem i.e. identification of task, little is known about the advantage of using task over session or query for search applications. It is defined to conduct immense analyses and comparisons to evaluate the efficacy of task track in search applications: user satisfaction determination, user search interest's prediction and related query suggestions.

Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision support (CDS) technologies based on ML need to be portable if they are to be adopted on a broad scale. In this respect, models developed at one institution should be reusable at another. Yet there are numerous examples of portability failure, particularly due to naive application of ML models. Portability failure can lead to suboptimal care and medical errors, which ultimately could prevent the adoption of ML-based CDS in practice. One specific healthcare challenge that could benefit from enhanced portability is the prediction of 30-day readmission risk. Research to date has shown that deep learning models can be effective at modeling such risk. In this work, we investigate the practicality of model portability through a cross-site evaluation of readmission prediction models. To do so, we apply a recurrent neural network, augmented with self-attention and blended with expert features, to build readmission prediction models for two independent large scale claims datasets. We further present a novel transfer learning technique that adapts the well-known method of born-again network (BAN) training. Our experiments show that direct application of ML models trained at one institution and tested at another institution perform worse than models trained and tested at the same institution. We further show that the transfer learning approach based on the BAN produces models that are better than those trained on just a single institution's data. Notably, this improvement is consistent across both sites and occurs after a single retraining, which illustrates the potential for a cheap and general model transfer mechanism of readmission risk prediction.

Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes. Despite the hype with AI, the full realization of this potential is seriously hindered by several systemic problems, including data privacy, security, bias, fairness, and explainability. In this paper, we propose a novel canonical architecture for the development of AI models in healthcare that addresses these challenges. This system enables the creation and management of AI predictive models throughout all the phases of their life cycle, including data ingestion, model building, and model promotion in production environments. This paper describes this architecture in detail, along with a qualitative evaluation of our experience of using it on real world problems.

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches.